license: openrail
language:
- en
tags:
- stable-diffusion
- lora
- txt2img
Model Card for yaozhi_character_lora
这是一个适用于Stable-diffusion的LORA模型，它被用来记住一个虚构人物“药指”的外貌特征，并在Stable-diffusion中的文生图等工作中尝试通过还原出这些特征来复现该虚拟人物。 This is a LORA model for Stable-diffusion, which is used to remember the characteristics of the fictional characters "YaoZhi" and to try to reproduce them by restoring them in work such as the Text to Image in Stable-diffusion.
Table of Contents
Table of Contents
- Model Card for yaozhi_character_lora
- Table of Contents
- Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Model Examination
- Environmental Impact
- Technical Specifications [optional]
- Citation
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
- How to Get Started with the Model
药指
角色
药指是一个具有操纵尸体能力的东方美少女道姑，常带着两股白色的发带，身着绿色中式长衫，白色长袖内衬，及膝白袜与布鞋。
来源
该角色的角色设计（外观，能力设定等）均出自艺术家FKEY笔下，并出现于其美术作品中，也见于衍生的同人美术与cosplay写真集中。
FKEY
插画艺术家，菲奇子、药指等诸多经典角色的缔造者，艺术家主页
Model Details
Model Description
基本信息
- Developed by: 0x910nni
- Model type: LORA
- Language(s) (NLP): en
- License: openrail
使用
Direct Use
权重
INS:1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0 包括旗袍领，长发，猫耳等特质， IND:1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0 更准确的上装信息 INALL:1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0 不会复现发带，发型分股，服装颜色信息， MIDD:1,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0 人物核心特征， OUTD:1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0
OUTS:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1 OUTALL:1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
Downstream Use [Optional]
Out-of-Scope Use
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Training Details
Training Data
More information on training data needed
Training Procedure
Preprocessing
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Speeds, Sizes, Times
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Model Examination
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation
BibTeX:
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APA:
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Glossary [optional]
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More Information [optional]
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Model Card Authors [optional]
0, x, 9, 1, 0, n, n, i
Model Card Contact
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How to Get Started with the Model
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Model Details
Model Description
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- Language(s) (NLP): [More Information Needed]
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Model Sources [optional]
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Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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APA:
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Glossary [optional]
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More Information [optional]
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Model Card Authors [optional]
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Model Card Contact
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